RMarkdown
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An Example
The code chunk below will download the data we’ll use in this analysis, and show the first 5 rows.
library(plm)
url <- "https://github.com/Matt-Brigida/FFIEC_Call_Reports/raw/master/1_querying_data_and_analysis/analyses/panel_data_analysis/full_panel/1_panel_with_full_quarter_date/1_one_panel_all_models/full_panel.rds"
panel <- readRDS(url(url))## theindex_panel quarter IDRSSD total_assets_lagged_1_year
## 37-20010331 20010331_37 20010331 37 NA
## 37-20010630 20010630_37 20010630 37 NA
## 37-20010930 20010930_37 20010930 37 NA
## 37-20011231 20011231_37 20011231 37 NA
## 37-20020331 20020331_37 20020331 37 65486
## 37-20020630 20020630_37 20020630 37 65427
## total_equity_lagged_1_year total_assets t1_LR_lagged_1_year
## 37-20010331 NA 65486 NA
## 37-20010630 NA 65427 NA
## 37-20010930 NA 65575 NA
## 37-20011231 NA 66624 NA
## 37-20020331 11424 68766 0.1798
## 37-20020630 11543 69304 0.1780
## t1_RBCR_lagged_1_year amt_CI_less_100_SB_loans_lagged_1_year
## 37-20010331 NA NA
## 37-20010630 NA NA
## 37-20010930 NA NA
## 37-20011231 NA NA
## 37-20020331 0.3245 NA
## 37-20020630 0.3012 0
## amt_CI_100_250_SB_loans_lagged_1_year
## 37-20010331 NA
## 37-20010630 NA
## 37-20010930 NA
## 37-20011231 NA
## 37-20020331 NA
## 37-20020630 0
## amt_CI_250_1000_SB_loans_lagged_1_year npa_30_89_lagged_1_year
## 37-20010331 NA NA
## 37-20010630 NA NA
## 37-20010930 NA NA
## 37-20011231 NA NA
## 37-20020331 NA 85
## 37-20020630 0 0
## npa_90_plus_lagged_1_year npa_nonacc_lagged_1_year npa_30_89
## 37-20010331 NA NA 85
## 37-20010630 NA NA 0
## 37-20010930 NA NA 0
## 37-20011231 NA NA 5
## 37-20020331 0 18 0
## 37-20020630 0 0 0
## npa_90_plus npa_nonacc net_income_lagged_1_year net_income
## 37-20010331 0 18 NA 220
## 37-20010630 0 0 NA 435
## 37-20010930 64 20 NA 679
## 37-20011231 0 0 NA 892
## 37-20020331 0 0 220 200
## 37-20020630 0 0 435 414
## domestic_deposits_lagged_1_year totSBloans_Delt
## 37-20010331 NA NA
## 37-20010630 NA NA
## 37-20010930 NA NA
## 37-20011231 NA NA
## 37-20020331 53715 NA
## 37-20020630 53502 NA
## totSBloans_Delt_lagged_1_year totNumSBloans_Delt
## 37-20010331 NA NA
## 37-20010630 NA NA
## 37-20010930 NA NA
## 37-20011231 NA NA
## 37-20020331 NA NA
## 37-20020630 NA NA
## totNumSBloans_Delt_lagged_1 tot_SB_loans_lagged_1_year
## 37-20010331 NA NA
## 37-20010630 NA NA
## 37-20010930 NA NA
## 37-20011231 NA NA
## 37-20020331 NA NA
## 37-20020630 NA 0
## tot_SB_loans_TA_lagged_1 less_100_lagged_SB_loans_TA
## 37-20010331 NA NA
## 37-20010630 NA NA
## 37-20010930 NA NA
## 37-20011231 NA NA
## 37-20020331 NA NA
## 37-20020630 0 0
## X100_250_lagged_SB_loans_TA X250_1000_lagged_SB_loans_TA
## 37-20010331 NA NA
## 37-20010630 NA NA
## 37-20010930 NA NA
## 37-20011231 NA NA
## 37-20020331 NA NA
## 37-20020630 0 0
## ROA ROA_lagged_1 tot_NPA tot_NPA_TA tot_NPA_lagged_1
## 37-20010331 0.003359497 NA 103 1.572855e-03 NA
## 37-20010630 0.006648631 NA 0 0.000000e+00 NA
## 37-20010930 0.010354556 NA 84 1.280976e-03 NA
## 37-20011231 0.013388569 NA 5 7.504803e-05 NA
## 37-20020331 0.002908414 0.003359497 0 0.000000e+00 103
## 37-20020630 0.005973681 0.006648631 0 0.000000e+00 0
## NPA_TA_lagged_1 TD_TA_lagged_1 mdi_ind asian_ind bhn_ind
## 37-20010331 NA NA 0 0 0
## 37-20010630 NA NA 0 0 0
## 37-20010930 NA NA 0 0 0
## 37-20011231 NA NA 0 0 0
## 37-20020331 0.001572855 0.8202517 0 0 0
## 37-20020630 0.000000000 0.8177358 0 0 0
## african_am_ind hispanic_ind born_vector de_novo
## 37-20010331 0 0 0 0
## 37-20010630 0 0 0 0
## 37-20010930 0 0 0 0
## 37-20011231 0 0 0 0
## 37-20020331 0 0 0 0
## 37-20020630 0 0 0 0
## TETA_lagged_1_year fin_crisis_ind post_crisis_ind
## 37-20010331 NA 0 0
## 37-20010630 NA 0 0
## 37-20010930 NA 0 0
## 37-20011231 NA 0 0
## 37-20020331 0.1744495 0 0
## 37-20020630 0.1764256 0 0
Summary Statistics
First we’ll create some tables of summary statistics to get a better understanding of our data set. Questions we would like to answer are:
For now:
- What is the capital structure of the average and median bank?
- What is the average proportion of loans of a given type (consumer, C&I, real-estate)—as a percent of assets?
- Is bank capital structure different for young firms?
- Do young firms focus on one type of loan?
- Were capital structures different during/after the financial crisis?
- What are the general characteristics of the average and median bank, i.e. ROA, NPAs
library(stargazer)
var <- c("quarter", "totSBloans_Delt", "totNumSBloans_Delt", "t1_LR_lagged_1_year", "tot_SB_loans_TA_lagged_1", "ROA_lagged_1", "NPA_TA_lagged_1", "total_assets_lagged_1_year", "TD_TA_lagged_1", "de_novo", "TETA_lagged_1_year", "post_crisis_ind", "fin_crisis_ind")
panel_vars <- data.frame(panel[, var])
panel_vars <- panel_vars[complete.cases(panel_vars), ]
de_novos <- subset(panel_vars, de_novo == 1)
not_de_novos <- subset(panel_vars, de_novo == 0)
## create tables with stargazer
stargazer(de_novos[, -1], type = "html", title="Descriptive Statistics: De Novo Banks", digits=3, out="html", covariate.labels = c("% Change Amt. S. Bus. Loans", "% Change Num. S. Bus. Loans", "Tier 1 Leverage Ratio", "Small-Business Loans", "ROA", "NPA", "Total Assets", "Deposits", "De Novo", "Total Equity", "Post Crisis", "Financial Crisis"))| Statistic | N | Mean | St. Dev. | Min | Pctl(25) | Pctl(75) | Max |
| % Change Amt. S. Bus. Loans | 12,813 | 0.451 | 4.493 | -1.000 | -0.099 | 0.303 | 326.410 |
| % Change Num. S. Bus. Loans | 12,813 | 0.523 | 7.257 | -1.000 | -0.079 | 0.306 | 703.488 |
| Tier 1 Leverage Ratio | 12,813 | 0.161 | 0.243 | -0.052 | 0.097 | 0.161 | 12.307 |
| Small-Business Loans | 12,813 | 0.046 | 0.059 | 0.000 | 0.005 | 0.065 | 0.633 |
| ROA | 12,813 | -0.001 | 0.012 | -0.244 | -0.002 | 0.004 | 0.237 |
| NPA | 12,813 | 0.001 | 0.004 | 0 | 0 | 0.001 | 0 |
| Total Assets | 12,813 | 810,306.200 | 3,988,093.000 | 3,350 | 66,654 | 331,980 | 119,678,000 |
| Deposits | 12,813 | 0.788 | 0.115 | 0.000 | 0.750 | 0.864 | 1.016 |
| De Novo | 12,813 | 1.000 | 0.000 | 1 | 1 | 1 | 1 |
| Total Equity | 12,813 | 0.149 | 0.100 | -0.052 | 0.099 | 0.160 | 1.000 |
| Post Crisis | 12,813 | 0.589 | 0.492 | 0 | 0 | 1 | 1 |
| Financial Crisis | 12,813 | 0.163 | 0.369 | 0 | 0 | 0 | 1 |
stargazer(not_de_novos[, -1], type = "html", title="Descriptive Statistics: Not De Novo Banks", digits=3, out="html", covariate.labels = c("% Change Amt. S. Bus. Loans", "% Change Num. S. Bus. Loans", "Tier 1 Leverage Ratio", "Small-Business Loans", "ROA", "NPA", "Total Assets", "Deposits", "De Novo", "Total Equity", "Post Crisis", "Financial Crisis"))| Statistic | N | Mean | St. Dev. | Min | Pctl(25) | Pctl(75) | Max |
| % Change Amt. S. Bus. Loans | 198,030 | -0.003 | 1.104 | -1 | -0.1 | 0.1 | 249 |
| % Change Num. S. Bus. Loans | 198,030 | 0.019 | 7.877 | -1 | -0.1 | 0.1 | 2,189 |
| Tier 1 Leverage Ratio | 198,030 | 0.103 | 0.034 | -0.098 | 0.085 | 0.113 | 1.575 |
| Small-Business Loans | 198,030 | 0.064 | 0.051 | 0.000 | 0.031 | 0.083 | 0.978 |
| ROA | 198,030 | 0.004 | 0.008 | -0.335 | 0.002 | 0.007 | 0.240 |
| NPA | 198,030 | 0.003 | 0.005 | 0.000 | 0.0001 | 0.003 | 0.287 |
| Total Assets | 198,030 | 535,806.300 | 2,538,888.000 | 2,190 | 88,650 | 372,096 | 157,935,238 |
| Deposits | 198,030 | 0.837 | 0.070 | 0.00003 | 0.807 | 0.884 | 1.101 |
| De Novo | 198,030 | 0.000 | 0.000 | 0 | 0 | 0 | 0 |
| Total Equity | 198,030 | 0.107 | 0.035 | -0.118 | 0.088 | 0.119 | 0.965 |
| Post Crisis | 198,030 | 0.450 | 0.498 | 0 | 0 | 1 | 1 |
| Financial Crisis | 198,030 | 0.175 | 0.380 | 0 | 0 | 0 | 1 |
Analyses
The Change in Small Business Loans
panel <- panel[, var]
panel <- panel[complete.cases(panel), ]
## orthogonalize TE
TE_ortho <- lm(panel$TETA_lagged_1_year ~ panel$t1_LR_lagged_1_year)$resid
FEmodel1 <- plm(totSBloans_Delt ~ t1_LR_lagged_1_year + TE_ortho + tot_SB_loans_TA_lagged_1 + ROA_lagged_1 + NPA_TA_lagged_1 + I(log(panel$total_assets_lagged_1_year)) + TD_TA_lagged_1 + post_crisis_ind + fin_crisis_ind, data = panel, model = "within", effect = "individual")
## summary(FEmodel1)
FEmodel2 <- plm(totSBloans_Delt ~ t1_LR_lagged_1_year + TE_ortho + tot_SB_loans_TA_lagged_1 + ROA_lagged_1 + NPA_TA_lagged_1 + I(log(panel$total_assets_lagged_1_year)) + TD_TA_lagged_1 + post_crisis_ind + fin_crisis_ind + I(log(panel$total_assets_lagged_1_year) * ROA_lagged_1), data = panel, model = "within", effect = "individual")
## summary(FEmodel3)
## add de novo
stargazer(FEmodel1, FEmodel2, covariate.labels = c("T1LR", "TE", "Small Business Loans", "ROA", "NPA", "ln(TA)", "Deposits", "Post Crisis", "Fin Crisis", "ln(TA) * ROA", "ln(TA) * NPA"), dep.var.labels = "% Change in Amt. SB Loans", digits = 3, no.space=TRUE, header=FALSE, type='html', omit.stat=c("LL"), title = "All Banks: Determinants of the % Change in the Amount of Small-Business Loans", out = "html", intercept.bottom = TRUE, notes = "Results are from fixed-effects models with bank fixed effects, for the years 2001 through 2017. Data are quarterly. The dependent variable is percent change in the amount of small-business loans. Small-Business loans are defined as the sum of commercial, industrial, and commercial real-estate loans. All variables are lagged one year relative to the dependent variable.")| Dependent variable: | ||
| % Change in Amt. SB Loans | ||
| (1) | (2) | |
| T1LR | 6.439*** | 6.335*** |
| (0.071) | (0.072) | |
| TE | 2.278*** | 2.080*** |
| (0.173) | (0.176) | |
| Small Business Loans | -3.419*** | -3.416*** |
| (0.125) | (0.125) | |
| ROA | -1.965*** | -31.128*** |
| (0.499) | (4.288) | |
| NPA | -1.544* | -1.445* |
| (0.815) | (0.815) | |
| ln(TA) | -0.459*** | -0.464*** |
| (0.011) | (0.011) | |
| Deposits | -1.197*** | -1.188*** |
| (0.098) | (0.098) | |
| Post Crisis | 0.036*** | 0.035*** |
| (0.008) | (0.008) | |
| Fin Crisis | 0.013 | 0.016* |
| (0.010) | (0.010) | |
| ln(TA) * ROA | 2.454*** | |
| (0.358) | ||
| Observations | 210,843 | 210,843 |
| R2 | 0.093 | 0.094 |
| Adjusted R2 | 0.049 | 0.050 |
| F Statistic | 2,300.083*** (df = 9; 201095) | 2,075.236*** (df = 10; 201094) |
| Note: | p<0.1; p<0.05; p<0.01 | |
| Results are from fixed-effects models with bank fixed effects, for the years 2001 through 2017. Data are quarterly. The dependent variable is percent change in the amount of small-business loans. Small-Business loans are defined as the sum of commercial, industrial, and commercial real-estate loans. All variables are lagged one year relative to the dependent variable. | ||
Can We Predict A Financial Crisis?
library(pglm)
summary(pglm(fin_crisis_ind ~ NPA_TA_lagged_1 + ROA_lagged_1, data = panel, family = binomial('probit')))## --------------------------------------------
## Maximum Likelihood estimation
## Newton-Raphson maximisation, 5 iterations
## Return code 1: gradient close to zero
## Log-Likelihood: -95437.61
## 4 free parameters
## Estimates:
## Estimate Std. error t value Pr(> t)
## (Intercept) -8.973e-01 3.904e-03 -229.86 <2e-16 ***
## NPA_TA_lagged_1 1.179e+01 5.634e-01 20.93 <2e-16 ***
## ROA_lagged_1 -2.134e+01 3.819e-01 -55.88 <2e-16 ***
## sigma 1.748e-11 8.040e-03 0.00 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## --------------------------------------------